PERFORMANCE OF GENETIC ALGORITHMS FOR SOLVING FLEXIBLE JOB-SHOP SCHEDULING PROBLEM (original) (raw)
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Job Shop Scheduling With Alternate Process Plan by Using Genetic Algorithm
Scheduling in a job-shop system is a challenging task, however it costly and time consuming. Successful implementation of automated manufacturing system highly depends on effective utilization of resource. Efficient scheduling algorithm for alternate process plan may increase the throughput rate, utilization of machine and guarantee a reasonable return of investment. In this paper, the objective is to prepare an alternate model with minimum makespan value by using genetic algorithm. For this makespan value to solve the various job sequencing problem, utilization of machine, cost of machine for a production shop that is characterized by alternate routing and flexible machines also investigates an optimization for scheduling job in a just-in-time environment. All jobs can be processed through alternate routing to be processed in a specified order of operation. Each operation has to be processed on one of a set of resources (e.g. machine) with possibly different efficiency and hence processing time. The objective is to minimize the make span of the job, mean flow time, utilization of machine, Average utilization of machine.
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Journal of Advanced Manufacturing Systems, 2011
The present work aims to develop a genetic algorithm-based approach to solve the scheduling optimization problem in the Job Shop manufacturing environment. A new encoding scheme for chromosome representation has been developed for this purpose that denotes a priority sequence of operations, from which a schedule can be generated if the precedence constraints are known. The successful implementation of the proposed encoding scheme has been presented and its performance has been compared with the existing operation-based scheme found in literatures across different test cases by varying the number of jobs and machines in the shop floor.
A Genetic Algorithm-Based Approach for Flexible Job Shop Scheduling
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Flexible job shop scheduling is a hard combinatorial optimization problem. This paper introduces a simulation-based Genetic Algorithm approach to solve flexible job shop scheduling problem. Four manufacturing scenarios have been considered to access the performance of a job shop with objective to minimize mean tardiness, mean flow time and makespan. Results show that multiple process plans performs better than single process plan for each job type and if only single process plan is made available, then process plan selected on the basis of minimum production time criterion yields better results than other criterion of randomly selected process plan and minimum number of set-ups. Moreover, embedding restart scheme into regular Genetic Algorithm results improvement in the fitness value.
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers & Operations Research, 2008
In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP. ᭧ Scheduling of operations is one of the most critical issues in the planning and managing of manufacturing processes. To find the best schedule can be very easy or very difficult, depending on the shop environment, the process constraints and the performance indicator [1]. One of the most difficult problems in this area is the Job-shop Scheduling Problem (JSP), where a set of jobs must be processed on a set of machines, each job is formed by a sequence of consecutive operations, each operation requires exactly one machine, machines are continuously available and can process one operation at a time without interruption. The decision concerns how to sequence the operations on the machines, such as a given performance indicator is optimized. A typical performance indicator for JSP is the makespan, i.e., the time needed to complete all the jobs. JSP is a well-known NP-hard problem .
Solving job-shop scheduling problems by genetic algorithm
Proceedings of IEEE International Conference on Systems, Man and Cybernetics
Job-shop Scheduling Problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used t o solve the problem is the branch-and-bound method, which takes considerable computing time when the size of problem is large. W e propose a new method for solving JSP using Genetic Algorithm (G A) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of G A are how t o represent the schedules as an individuals and t o design the genetic operators for the representation in order t o produce better results.
E3S Web of Conferences, 2021
This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.
Genetic Algorithm for Job Shop Scheduling Problem: A Case Study
The job-shop scheduling (JSS) is a schedule planning for low volume systems with many variations in requirements. In job-shop scheduling problem (JSSP), there are k operations and n jobs to be processed on m machines with a certain objective function to be minimized. Due to complexity of transferring work in process product, this research add transfer time variable from one machine to another for each different operation. Performance measures are mean flow time and make span. In this paper we used genetic algorithm (GA) with some modifications to deal with problem of job shop scheduling. The result than is compared with dispatching rules such as longest processing time, shortest processing time and first come first serve. The numerical example showed that GA result can outperform the other three methods.
An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem
European Journal of Operational Research, 2010
The Distributed and Flexible Job-shop Scheduling problem (DFJS) considers the scheduling of distributed manufacturing environments, where jobs are processed by a system of several Flexible Manufacturing Units (FMUs). Distributed scheduling problems deal with the assignment of jobs to FMUs and with determining the scheduling of each FMU, in terms of assignment of each job operation to one of the machines able to work it (job-routing flexibility) and sequence of operations on each machine. The objective is to minimize the global makespan over all the FMUs. This paper proposes an Improved Genetic Algorithm to solve the Distributed and Flexible Job-shop Scheduling problem. With respect to the solution representation for non-distributed job-shop scheduling, gene encoding is extended to include information on job-to-FMU assignment, and a greedy decoding procedure exploits flexibility and determines the job routings. Besides traditional crossover and mutation operators, a new local search based operator is used to improve available solutions by refining the most promising individuals of each generation. The proposed approach has been compared with other algorithms for distributed scheduling and evaluated with satisfactory results on a large set of distributed-and-flexible scheduling problems derived from classical job-shop scheduling benchmarks.
Flexible job-shop scheduling is a type of scheduling which is extension of Job-shop scheduling problem. In FJSP, operations are processed on different machines, which means operations are break down to sublots, and these sublots are processed by machines independently. In previous research, mathematical model was developed along with implementation of Genetic Algorithm. This paper gives an overview of improved methods to Flexible Job-Shop scheduling Problem with overlapping in operation[1].
Modified genetic algorithm for job-shop scheduling: A gap utilization technique
2007 Ieee Congress on Evolutionary Computation, 2007
The Job-Shop Scheduling Problem (JSSP) is one of the most critical combinatorial optimization problems. The objective of JSSP in this research is to minimize the makespan. In this paper, we propose two Genetic Algorithm (GA) based approaches for solving JSSP. Firstly, we design a simple heuristic to reduce the completion time of jobs on the bottleneck machines that we call the reducing bottleneck technique (RBT). This heuristic was implemented in conjunction with a GA. Secondly; we propose to fill any possible gaps left in the simple GA solutions by the tasks that are scheduled later. We call this process the gap-utilization technique (GUT). With GUT, we also apply a swapping technique that deals only with the bottleneck job. We study 35 test problems with known solutions, using the existing GA and our proposed two algorithms. We obtain optimal solutions for 23 problems, and the solutions are very close for the rest.